# -*- coding: utf-8 -*-
import os

from keras.layers import Flatten, Input
from keras.layers.convolutional import Conv2D, Conv2DTranspose, ZeroPadding2D
from keras.layers.core import Activation, Dense, Dropout, Permute, Reshape
from keras.layers.merge import concatenate
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import AveragePooling2D, GlobalAveragePooling2D
from keras.layers.wrappers import TimeDistributed
from keras.models import Model
from keras.regularizers import l2
from keras.utils import plot_model

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'


def conv_block(input, growth_rate, dropout_rate=None, weight_decay=1e-4):
    x = BatchNormalization(axis=-1, epsilon=1.1e-5)(input)
    x = Activation('relu')(x)
    x = Conv2D(growth_rate, (3, 3),kernel_initializer='he_normal', padding='same')(x)
    if(dropout_rate):
        x = Dropout(dropout_rate)(x)
    return x


def dense_block(x,nb_layers,nb_filter,growth_rate,droput_rate=0.2,weight_decay=1e-4):
    for i in range(nb_layers):
        cb = conv_block(x,growth_rate,droput_rate,weight_decay)
        x = concatenate([x,cb],axis=-1)
        nb_filter +=growth_rate
    return x ,nb_filter

def transition_block(input,nb_filter,dropout_rate=None,pooltype=1,weight_decay=1e-4):
    x = BatchNormalization(axis=-1,epsilon=1.1e-5)(input)
    x = Activation('relu')(x)
    x = Conv2D(nb_filter,(1,1),kernel_initializer='he_normal', padding='same', use_bias=False,
               kernel_regularizer=l2(weight_decay))(x)

    if(dropout_rate):
        x = Dropout(dropout_rate)(x)

    if(pooltype==2):
        x = AveragePooling2D((2,2),strides=(2,2))(x)
    elif(pooltype==1):
        x = ZeroPadding2D(padding=(0,1))(x)
        x = AveragePooling2D((2,2),strides=(2,1))(x)
    elif(pooltype==3):
        x = AveragePooling2D((2,2),strides=(2,1))(x)
    return x,nb_filter


def dense_cnn(input, nclass):

    _dropout_rate = 0.2
    _weight_decay = 1e-4

    _nb_filter = 64
    # conv 64  5*5 s=2
    x = Conv2D(_nb_filter, (5, 5), strides=(2, 2), kernel_initializer='he_normal', padding='same',
               use_bias=False, kernel_regularizer=l2(_weight_decay))(input)

    # 64 +  8 * 8 = 128
    x, _nb_filter = dense_block(x, 8, _nb_filter, 8, None, _weight_decay)
    #128
    x, _nb_filter = transition_block(x, 128, _dropout_rate, 2, _weight_decay)

    #128 + 8 * 8 = 192
    x, _nb_filter = dense_block(x, 8, _nb_filter, 8, None, _weight_decay)
    #192->128
    x, _nb_filter = transition_block(x, 128, _dropout_rate, 2, _weight_decay)

    #128 + 8 * 8 = 192
    x, _nb_filter = dense_block(x, 8, _nb_filter, 8, None, _weight_decay)

    x = BatchNormalization(axis=-1, epsilon=1.1e-5)(x)
    x = Activation('relu')(x)

    x = Permute((2, 1, 3), name='permute')(x)
    x = TimeDistributed(Flatten(), name='flatten')(x)
    y_pred = Dense(nclass, name='out', activation='softmax')(x)

    basemodel = Model(inputs=input,outputs=y_pred)
    basemodel.summary()
    return basemodel


if __name__ == '__main__':
    input = Input(shape=(32, 153, 1), name='the_input')
    cnn = dense_cnn(input, 1000)
    cnn.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])
    plot_model(cnn, to_file='./models/densenet.png')
    cnn.summary()
